Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review

Abstract Background and Aims Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three‐dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis. Methods We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings. Results and Conclusion The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL‐based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation.


| INTRODUCTION
Dementia is a collection of diseases with increasing signs and symptoms, with Alzheimer's disease (AD) being the most common.AD is a medical condition defined by a gradual decline in thinking and memory functions.It is a prevalent condition amongst older individuals, constituting around 60%-80% of dementia cases.The recurrence rate of AD is substantial; however, no remedy is available.Over five million individuals globally have dementia, with 70% being AD people.The global frequency of AD was 30 million in 2006 but is expected to triple by 2050. 1 The intensity of AD could lead to the death of the person.
During the early phase, memory impairments are relatively minor. 1 Figure 1 shows the different signs/indications of AD.However, as AD progresses, victims gradually lose their capacity to communicate and interact with their environment.In the later phases of the disease, this can ultimately result in a complete loss of senses for a long time.
Initial detection of the disease facilitates appropriate patient treatment, therefore reducing the detrimental impact of AD and slowing the progression towards dementia. 2Figure 2 shows the different stages of AD.
Magnetic resonance imaging (MRI) can differentiate between gray matter (GM) and white matter (WM).The hippocampus is a vital brain area for memory and learning. 4The hippocampus shrinks dramatically during mild cognitive impairment (MCI) to AD. 5 MRI is the most commonly utilized method for neuroimaging due to its lack of radioactive tracer substances or hazardous gamma rays, distinguishing it from other forms of imaging. 6Treatment could stop the development of disease symptoms.Therefore, researchers in this field apply deep learning (DL) approaches. 7,8DL is data-hungry; as more data is added, DL algorithms become more effective and surpass conventional approaches like the human brain. 9,10Earlier studies have shown a leading use of two-dimensional (2D) images with DL techniques.Due to spatial information loss in the brain's cube, 2D MRI scan slicing has drawbacks. 11Previously, using 2D data, which converts three-dimensional (3D) MRI images into 2D slices and uses them for feature extraction, was typical.Converting 3D to 2D loses feature detail.Straight 3D feature extraction may reveal information about the features because 3D input improves system efficiency. 12The three planes of 3D neuroimaging MRI are shown in Figure 3.
The majority of previous research on AD detection used 2D images, with very little research using 3D images. 14The latest developments in neural network topologies, data enhancement methods, and powerful GPUs allow 3D deep learning to interpret F I G U R E 2 Alzheimer's stages from left to right are normal, mild, and severe. 3oxel clinical data.Therefore, throughout the past decade, there has been a significant increase in the use of 3D deep learning in various medical imaging techniques.In this study, we provide a comprehensive analysis of the uses of DL approaches with 3D imaging for diagnosing AD and potential areas for further research.As far as we know, research articles are available online.However, this is the first review study explicitly focusing on DL techniques used with 3D imaging for AD detection.

| Search strategy
The research examines the acknowledgment of efforts to detect AD in several scientific databases, including IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and other electronic library databases.The analysis focuses on papers that include keywords such as "deep learning in Alzheimer's," "Alzheimer's diagnosis using deep learning," "Alzheimer's detection using deep learning with 3D imaging," and "Alzheimer's diagnosis and prediction using supervised deep learning."We searched for research articles regarding AD detection using DL, which can be up to 25 pages in Google Scholar.
Using the keywords mentioned above and reputable journals, we queried the Google Scholar database to include all publications, including those not found in the specified repositories.In this review article, we examine articles from 2020 to 2024.This research method follows the rules in Figure 4 of the PRISMA guidelines, ensuring the information collection process is organized and understandable.Moreover, Table 1 provides comprehensive information about the criteria for including and excluding data in PRISMA.This table thoroughly summarizes the criteria for deciding whether to review or exclude an item from the examination.At the onset of the study, we gathered papers from online database searches and chose 413 articles for the review.
Following a thorough analysis, we selected 87 publications for review.Among these, 31 articles presented different ideas, descriptions, and explanations of theories and models, while the remaining 56 articles mainly focused on application problems and current breakthroughs.Our dedication to delivering a sophisticated and innovative assessment has prompted us to focus on works published in recent years intentionally.

| Previous research gaps
Previous research on the application of DL to find AD in 3D images mainly focused on using CNN models and other DL models to get features and do classification tasks.However, further research is necessary to explore the capabilities of more recent structures, primarily designed to handle 3D MRI images.It is also essential to do more research using heterogeneous data fusion approaches.These involve combining data from different imaging types, like structural magnetic resonance imaging (sMRI), functional MRI (fMRI), and positron emission tomography (PET) imaging.This integration aims to improve the reliability and precision of the algorithms used for detecting AD.Enhanced GPUs, suitable system hardware, and adequately designed algorithms can increase implementation reliability.The comprehensibility of DL techniques in this field requires further research, limiting their use in healthcare.Addressing these inconsistencies can enhance the efficiency of detection and treatment methods for AD.

| Contributions
The review article has the following contributions: ▪ Implementation of DL methods with 3D imaging to detect AD from 2020 to 2024.▪ 3D images help identify and characterize brain deterioration by providing a full view of the brain area.
▪ We used 3D imaging to improve spatial resolution and understand complicated structures from many perspectives, which aids in accurately detecting AD.
▪ We explored 3D imaging with DL techniques to identify AD and its stages, using different datasets, imaging modalities, and core findings.
F I G U R E 3 A three-dimensional brain magnetic resonance imaging shows three planes (sagittal, coronal, and axial) from left to right. 13 I G U R E 4 PRISMA flow diagram of the article selection procedure.

Inclusion criteria Exclusion criteria
Research Research papers that insufficiency of information.
Settings DL in medical imaging.Not in medical imaging settings.

Region
Not restricted to an exact region.NA Abbreviations: 3D, three-dimensional; DL, deep learning; NA, not available.
This review further discusses popular neuroimaging modalities, 3D imaging with DL approaches for Alzheimer's detection, discussion, challenges in AD, conclusion, and future extensions in this area.

| BRAIN IMAGING MODALITIES FOR ALZHEIMER'S
According to previous research, numerous imaging procedures exist to help identify AD.However, the most prevalent and well-acknowledged methods among experts are PET and MRI.The text concisely explains how MRI works, including its many patterns and PET imaging.It also discusses how both techniques are used in AD classification.

| MRI imaging
MRI is a significant advancement in clinical imaging.MRI provides excellent spatial detail and superficial tissue resolution, allowing for a 3D tomographic view and the ability to show how the body changes constantly. 6Figure 5 presents a variety of MRI series images.
The application of sMRI in the detection of AD is essential.This noninvasive imaging technique provides accurate information about the actual structure and condition of the brain as a whole.Here, we aim to define transformers as models that can be classified into supervised or unsupervised deep learning, depending on the specific application.Figure 7 shows the different steps of DL methods researchers used for their work.
The following DL with 3D image data for AD detection is described in detail.

| 3D imaging for AD detection
DL techniques use 3D images to evaluate and retrieve data for AD diagnosis.We present the details of various models used with 3D imaging in diverse domains.Table 2 presents the supervised DL with 3D imaging, which has the data set name, imaging modality, model, and critical findings.
Different methods have been applied in the article, and each has its pros and cons regarding its structural nature.Table 3 shows the algorithms used in this article and their advantages and limitations.possibility of transformers in 3D healthcare imaging, renowned for their ability to handle sequences and attention-intensive tasks.This is mainly due to their impressive feature retrieval capability.CNNs, enhanced by GANs and VAEs, currently provide the highest level of performance in the AD field utilizing 3D imaging.

| CHALLENGES
Although DL techniques have yielded positive results, identifying AD still faces several issues that require attention.The data augmentation and transfer learning models have stopped overfitting problems in the research and group of information.However, issues with generalization can still happen when there are not enough data items. 80The data set will be expanded.However, its efficiency remains uncertain, and more studies will continue in this domain.Supervised DL has mitigated this issue and decreased the reliance on professional expertise, but additional research is necessary.Despite DL methods' impressive achievements, AD identification faces several restrictions and challenges. 81A comprehensive understanding of the depth method, benchmarking platform, and other relevant factors is necessary to determine the ideal balance of several biomarkers.Several fusion approaches have the potential to contribute to research on AD. 82,83 Still, there are obstacles to overcome when using DL-based techniques [84][85][86][87][88][89][90][91] nature Original research articles Thesis, communication letters, white papers, editorials, and reports Language applied Research papers are presented in English.Duplicate and non-English research articles Publication years Articles published from 2020 to 2024 (for applications and results analysis part) Not related to the subject of the review Intervention DL and 3D images Traditional and arithmetical methods Source of articles Articles published in academic journals and conferences.

Figure 6 ▪
Alzheimer's stages on PET images.Regular PET investigations are not feasible due to the radioactive elements employed in scanning.FDGPET is the most commonly used PET for detecting AD.The PET method requires immediate imaging after injecting the radioactive tracer substances, making it F I G U R E 5 Different magnetic resonance imaging (MRI) sequence images were left to right: first (A) functional MRI (fMRI), (B) diffusion tensor imaging (DTI), (C) diffusion-weighted imaging (DWI), and the last (D) T1-W MRI. 15 F I G U R E 6 A positron emission tomography (PET) image of Alzheimer's disease (AD), cognitively normal (CN), and mild cognitive impairment (MCI) patients from left to right.time-sensitive.The device captures photons, creating 3D images of the subject. 20Electroencephalograms, MRIs, and PETs are standard neuroimaging techniques used to study brain function and make diagnoses.Since MRI and PET became unaffordable, EEG became the preferred diagnostic method. 213 | ALZHEIMER'S DIAGNOSIS WITH DL APPROACHES DL's untapped potential is gaining significant interest in clinical studies.DL enables algorithms using computation and multi-processing levels to acquire a collection of attributes from source data and classify the result based on the learned data. 22DL is suggested for initial AD detection due to its superior image accuracy for classification compared to the classic machine learning approach. 23,24Unsupervised learning uses training data without labels.The following are the most commonly used unsupervised learning models: ▪ Autoencoder (AE) ▪ Deep Belief Network (DBN) Generative adversarial networks (GANs) ▪ Restricted Boltzmann Machines (RBMs) ▪ Variational Autoencoders (VAEs) In supervised DL, methods are learned to map input information into output labels using pairs of inputs and outputs from the training process.The core models and their variants are primarily used as; ▪ Convolutional Neural Network (CNN) ▪ Recurrent Neural Network (RNN) ▪ Long short-term memory (LSTM) ▪ Gated Recurrent Unit (GRU) ▪ Capsule Networks ▪ Transformers Little sensitivity and F1 score in detecting the shift from mild to AD 16 17-weighted MRI is utilized for structural evaluation and T2 for diagnostic investigation.Circulation variations in fMRI measure activity in the brain.Scientists often use fMRI to study neurological diseases such as bipolar disorder, and they are adopting it for further disease diagnosis.17Diffusiontensor imaging (DTI) is an anisotropic diffusion technique employed in MRI to evaluate the organization of the brain's axonal tissue network.The last figure displays the maxi- 19on, classification, therapeutic management, and cerebral glucose metabolism detection.PET frequently demonstrates the development of AD, including the phases of MCI.19 A B L E 3 DL methods and their advantages and disadvantages.: AE, Autoencoder; 3D CNN, three-dimensional convolutional neural network; DBN, Deep Belief Network; DL, deep learning; GANs, generative adversarial networks; RBM, Restricted Boltzmann Machines; RNN, recurrent neural networks.
Muhammad Faheem: Methodology; data curation, formal analysis; visualization, review and editing.T Abbreviations